Praxis Global Alliance: Next gen business advisory using data science, technology, business research and on ground domain experience

From Analytics to Insights: A joint mission by Business & Analyst teams

The journey from Data Analytics to Data Insights is too important to be left to the statisticians/techies/data analysts alone (no offence meant!) – it has to be owned jointly with the Business teams who ideally should have the conceptual clarity & contextual familiarity to sift through the findings.

A leading FMCG company was witnessing unusually heavy usage (& repeat demand)from its retailers about a specific customer-facing promotional flyer.The marketing team was, of course, happy with the communication elements used & termed it a huge success. The Finance & Sales teams’ reaction was somewhat muted, as it was not translating into proportionate sales. The “analytics” team looked at the campaign structure, offer T&Cs, discount workings, secondary off-take data for the last 2 quarters, similar offers from competitors, market-share changes in the same period, and what not, but still could not figure out a real reason, within an acceptable confidence-level range.

Top management was understandably confused. So they sent an old hand to investigate; who toured the market and submitted his two-line finding – the paper quality of the promotional flyers was really good & retailers were using it for doing their daily inventory checks!Some enterprising retailers had actually created tear-away notepads from these marketing flyers!

Welcome to Data analytics – the buzzword these days (of course, along with quite a number of other popular buzzwords). Some organisations, by their very nature of business, are data rich; they have been used to collecting data over the years (about customer profile, transaction history, stated preferences, etc.).

In spite of tons of such data points, there was no easy way to make actionable business sense out of it.

There still isn’t!

Quite a number of tech-driven start-ups have sprung up in the last couple of years, sometimes, promising a lot more than can be realistically delivered. Some of the main reasons for these are typically:

Organizations are not clear about what they want.They generally hand over their zealously guarded data (sometimes not all of it) and somehow expect to see magical outputs, as actionable insights.

The Analytics team is comprised mostly of statisticians/techies/data analysts (or whatever fancy titles are in vogue; no offence meant!). These teams sometimes lack the business understanding which is essential to look beyond the dataand still be coherent with the analytics output.

While coming up with a data analytics strategy, it bodes well to keep the following things in mind:

Not every data point which can be collected or has been collected, needs to be collected. If organizations were to take this approach of storing every data point they can, in the hope that it would be useful in the future, soon they would be spending disproportionate amounts in storage/access/processing costs compared to the potential additional business. So take a long, hard look at the data points. Agreed, it may not be possible to know everything upfront before embarking on your data analytics journey; but it is worthwhile to be acutely aware of this.

Not every data point needs to be collected, at all points in time. Customers’ needs, preferences, patterns, relationships, etc. change over a period of time. While some are event-based/trigger-based, some could be periodic (maybe with varying duration). Even if we have identified some data points as important to be collected, we should also know when or till when it should be done.

Data points, in itself, have limited utility. More than the number of data points, the correlation between them matters more. Of course, the data points have to be statistically significant to do any dependable analysis. However, given enough data points over a reasonable time-frame, it should be theoretically possible to arrive a non-zero correlation between the daily temperature fluctuations during the trading time window and the variance of stock market indices! But as common sense might tell you, relying just on that will not be 100% accurate. Not unless there is a massive heat-stroke of the century on that day!

The analytics approach should be amenable to changes over time. You’ve identified the correct data points, you’ve identified the correct frequency & duration of collection and you’ve also identified the co-relation between these variables. But over a period of time, these co-relations itself might change. There also might be some new factors which materially influence the existing variables. It should be possible to include these in the analytics approach, as and when encountered.

The incident shared above underlines the importance of real-world insightsthat are necessary to put the analytics results in the right context. Just having the correlations identified based on past data is not sufficient; it has to be endorsed by on-the-ground business learnings. Only then will analytics become insights!